VLLM

Learn how to provision, optimize, and serve the massive Qwen3.8-2.4T-A95B model on Amazon SageMaker HyperPod using vLLM, NVFP4 quantization, and MTP speculative decoding.

A comprehensive performance and cost benchmark of 30B Mixture-of-Experts (MoE) models on AWS SageMaker AI across G5, G6, G6e, and next-gen G7 instances powered by NVIDIA Blackwell GPUs.

A deep technical analysis of implementing Speculative Decoding in vLLM on AMD ROCm GPUs, covering hardware execution dynamics, benchmark comparisons, code implementations, and optimization strategies for high-throughput LLM serving.

Learn how to build high-speed desktop agents using a hybrid architecture: local dual-GPU vLLM for screen perception and coordinate grounding, paired with cloud-based reasoning LLMs via n1n.ai.

Setting LLM temperature to 0 does not guarantee deterministic output. Discover how GPU dynamic batching, CUDA kernel non-associativity, and concurrency cause up to 30 percent output variance in identical runs.

An in-depth analysis of Qwen3.8-27B, evaluating its performance across code, reasoning, and structured JSON tasks, alongside hardware requirements and API integration strategies.

A deep dive into the architecture, performance benchmarks, and decision frameworks for choosing between vLLM and Ollama for LLM inference in 2026.

Learn the real arithmetic behind GPU memory for LLMs, focusing on the KV cache, fragmentation, and overhead that most tutorials skip.

The AI ecosystem is mirroring the 2015 container wars. As open-weight models like Llama, Qwen, and DeepSeek-V3 reach frontier-level performance, the center of gravity is shifting from proprietary APIs to an open, customizable substrate. Learn why developers must adapt now.

An in-depth look at how the native vLLM backend for Transformers revolutionizes LLM inference speed and efficiency for developers.

A deep dive into model quantization formats like AWQ, GPTQ, and GGUF, and how to implement high-performance serving using vLLM and Dynamic LoRA for enterprise Small Language Models (SLMs).

A deep dive into deploying high-performance vLLM servers using Hugging Face Jobs, including technical configurations, benchmarking, and cost-effective API strategies.